The research and development of electric vehicles (EVs) have been accelerated at unprecedented pace in recent years, driven primarily by their energy efficiency and environmental benefits. At local level, EVs do not emit any pollutants or consume any gasoline, and in combination with electricity from renewable energy, they could achieve low emission and fuel consumption on a well-to-wheel basis. Nevertheless, despite the numerous advantages of EVs over internal combustion engine (ICE) based conventional vehicles, the performance of EVs is still limited due to the challenges in the development of reliable, low cost and long life cycle battery systems.
While scientists are continuously looking for new materials to build next-generation batteries with even higher energy and power density, there are many difficulties to be solved for the battery management and system integration. The two most important tasks are known as the state-of-charge (SOC) estimation and state-of-health (SOH) determination, and both have been studied extensively in the literature. SOC is commonly defined as “the percentage of the maximum possible charge that is present inside a rechargeable battery” and SOH is “a ‘measure’ that reflects the general condition of a battery and its ability to deliver the specified performance in comparison with a fresh battery”. Typically the quantitative definition of SOH is based either on the battery capacity or the internal resistance depending on specific applications.
Many methods for on-line SOC estimation have been studied including coulomb counting, open circuit voltage-SOC (OCV-SOC) mapping and model based approach with extended Kalman filter (EKF). In contrast, the development of an on-line SOH monitoring technique is more challenging because of the complicated electrochemical mechanism involved in battery aging. Whereas it is possible to assess the resistance growth issue by both off-line test such as electrochemical impedance spectroscopy (EIS) and on-line identification algorithms such as the use of least squares method, the detection of capacity fading still largely relies on laboratory measurements and off-line analysis.
One conventional and most common method in determining battery capacity fading is based on the OCV-SOC curve. However, it requires fully charging or discharging the battery at low rate (e.g., 1/25C) or measuring the open circuit voltage after a long relaxation period (e.g., more than 2 hours) at SOC levels that span the entire range. Both methods require time-consuming tests and thus are not applicable for on-board implementation with real-life operation data. An alternative approach of studying capacity loss is the so-called incremental capacity analysis (ICA). ICA transforms voltage plateaus, which is related to a first-order phase transformation, or inflection points, which is associated with a formation of solid solution, on charging/discharging voltage (V-Q) curve into clearly identifiable dQ/dV peaks on incremental capacity (IC) curve. The concept of ICA originally came from the intercalation process of lithium and the corresponding staging phenomenon at the graphite anode. ICA has the advantage to detect a gradual change in cell behavior during a life-cycle test, with greater sensitivity than those based on conventional charge/discharge curves and yield key information on the cell behavior associated with its electrochemical properties. Although ICA was proved to be an effective tool for analyzing battery capacity fading, most studies have focused on understanding the electrochemical aging mechanism and no study has been reported on the real-time application of ICA. Meanwhile, since all the peaks on an IC curve lie within the voltage plateau region of the V-Q curve, which is relatively flat and more sensitive to measurement noise, calculating dQ/dV directly from data set could be difficult. Hence, effective and robust algorithms of obtaining the IC curve need to be developed.
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